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Exploring the Most Useful Features for Authorship Attribution - Paper","","Authorship attribution methods identify document authors by analyzing individuals' writing style and preferred topics, yet prior work has not systematically studied how dataset characteristics influence which feature types work best. This study analyzes four widely used datasets to determine how style, content, and hybrid features affect accuracy under varying conditions. Findings are used to build discrete and continuous authorship attribution models and extend a neural approach, improving results on two datasets over prior state of the art.",{"@graph":69,"@context":118},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/topic-or-style-exploring-the-most-useful-features-for-authorship-attribution-paper/501868/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/topic-or-style-exploring-the-most-useful-features-for-authorship-attribution-paper/501868.png","ImageObject",300,407,{"name":92,"@type":93},"gewufjwa","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114],{"name":109,"@type":110,"acceptedAnswer":111},"Which feature types are compared in the study?","Question",{"text":112,"@type":113},"The paper considers three feature types: style, content, and hybrid features that combine aspects of both.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What is the key finding about character n-grams?",{"text":117,"@type":113},"Character n-grams do not perform equally well across all datasets; their usefulness depends on dataset characteristics, and the same trend holds for discrete and continuous representations.","https://schema.org",{"og:url":83,"og:type":120,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":122,"canonical":83},"index,follow",{"doc_id":124,"site_id":62},501868,1791026267,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":92,"user_avatar":129,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":135,"language":136,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":67,"update_tm":140,"read_time":141},3985747882900,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Topic or Style? Exploring the Most Useful Features for Authorship  \nAttribution  \nYunita Sari, Mark Stevenson and Andreas Vlachos Department of Computer Science  \nUniversity of Sheffield, UK  \nfy.sari, mark.stevenson, [a.vlachos](a.vlachosg@sheffield.ac.uk)[g](a.vlachosg@sheffield.ac.uk)[@sheffield.ac.uk](a.vlachosg@sheffield.ac.uk)  \nAbstract  \nApproaches to authorship attribution, the task of identifying the author of a document, are based on analysis of individuals' writing style and/or preferred topics. Although the problem has been widely explored, no previous studies have analysed the relationship between dataset characteristics and effectiveness of different types of features. This study carries out an analysis of four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. The results of the analysis are applied to authorship attribution models based on both discrete and continuous representations. We apply the conclusions from our analysis to an extension of an existing approach to authorship attribution and outperform the prior state-of-the-art on two out of the four datasets used.  \n1 Introduction  \nAuthorship attribution plays an important role in many applications, including plagiarism detection and forensic investigation. Approaches to this problem attempt to identify a document's author through analysis of individual's writing style and/or topics they tend to write about. The problem has been extensively studied and a wide range of features has been explored (Stamatatos, 2013; Schwartz et al., 2013; Seroussi et al., 2013; H¨urlimann et al., 2015) . However, there has been a lack of analysis of the behavior of features across multiple datasets or using a range of classifiers. Consequently, it is difficult to determine which types of features will be most useful for a particular authorship attribution dataset.  \nAuthorship attribution is a unique task which is closely related to both the representation of individuals' writing style and text categorization. In some cases, where there is a clear topical distinction between the documents written by different authors, content-related features such as those used in text categorization may be effective. However, style-based features are more likely to be effective for datasets containing a more homogeneous set of topics. Previous work on feature exploration for authorship attribution, focused on the overall effectiveness of features without considering the characteristics of the datasets to which they were applied, e.g. (Grieve, 2007; Guthrie, 2008; Stamatatos, 2009; Brennan et al., 2012; Sapkota et al., 2015) . A wide range of features have been applied to the authorship attribution problem and many previous studies concluded that using character n-grams is often effective, e.g. (Penget al., 2003; Koppel et al., 2011; Schwartz et al., 2013; Sapkota et al., 2015; Sari et al., 2017; Shrestha et al., 2017) . Thus, character n-grams have become the go-to features for this task to capture both an author's topical preferences and writing style.  \nThis study explores how the characteristics of a dataset affect the usefulness of different types of features for the authorship attribution task. Experiments are carried out using four datasets that have previously been widely used for this task. Three types of features are considered: style, content and hybrid (a mixture of the previous two types) . In contrast to previous work, this study finds that character n-grams do not perform equally well in all datasets. The analysis holds for authorship attribution models using discrete and continuous representations. Using topic modeling and feature analysis, the most effective features can be successfully predicted for three of the four datasets. The results of this analysis  \nThis work is licensed under a Creative Commons Attribution 4 .0 International Licence. Licence details: [http://](http://)[ ](http://)[creativec","cbCaibcFJLZ2YpHR","https://ap.wps.com/l/cbCaibcFJLZ2YpHR","pdf",880360,11,"English","# Introduction\n## Feature usefulness across datasets\n# Related Work\n## Stylometric and feature categories","[{\"question\":\"Which feature types are compared in the study?\",\"answer\":\"The paper considers three feature types: style, content, and hybrid features that combine aspects of both.\"},{\"question\":\"What is the key finding about character n-grams?\",\"answer\":\"Character n-grams do not perform equally well across all datasets; their usefulness depends on dataset characteristics, and the same trend holds for discrete and continuous representations.\"}]","Topic or Style? Exploring the Most Useful Features for Authorship Attribution - Paper | PDF",1791026088,28]